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48 lines
1.6 KiB
Markdown
48 lines
1.6 KiB
Markdown
# Support Vector Classification
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Classifier implementing Support Vector Machine based on libsvm.
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### Constructor Parameters
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* $kernel (int) - kernel type to be used in the algorithm (default Kernel::LINEAR)
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* $cost (float) - parameter C of C-SVC (default 1.0)
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* $degree (int) - degree of the Kernel::POLYNOMIAL function (default 3)
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* $gamma (float) - kernel coefficient for ‘Kernel::RBF’, ‘Kernel::POLYNOMIAL’ and ‘Kernel::SIGMOID’. If gamma is ‘null’ then 1/features will be used instead.
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* $coef0 (float) - independent term in kernel function. It is only significant in ‘Kernel::POLYNOMIAL’ and ‘Kernel::SIGMOID’ (default 0.0)
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* $tolerance (float) - tolerance of termination criterion (default 0.001)
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* $cacheSize (int) - cache memory size in MB (default 100)
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* $shrinking (bool) - whether to use the shrinking heuristics (default true)
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* $probabilityEstimates (bool) - whether to enable probability estimates (default false)
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```
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$classifier = new SVC(Kernel::LINEAR, $cost = 1000);
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$classifier = new SVC(Kernel::RBF, $cost = 1000, $degree = 3, $gamma = 6);
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```
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### Train
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To train a classifier simply provide train samples and labels (as `array`). Example:
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```
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use Phpml\Classification\SVC;
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use Phpml\SupportVectorMachine\Kernel;
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$samples = [[1, 3], [1, 4], [2, 4], [3, 1], [4, 1], [4, 2]];
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$labels = ['a', 'a', 'a', 'b', 'b', 'b'];
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$classifier = new SVC(Kernel::LINEAR, $cost = 1000);
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$classifier->train($samples, $labels);
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```
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### Predict
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To predict sample label use `predict` method. You can provide one sample or array of samples:
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```
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$classifier->predict([3, 2]);
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// return 'b'
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$classifier->predict([[3, 2], [1, 5]]);
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// return ['b', 'a']
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```
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